activity
20232026
most citedCan Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE

3 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

cs.HC2026

Toward Template-Free Explainability for Monte Carlo Tree Search

Siqi Lu, Mirsaleh Bahavarnia, Hiba Baroud +3

Probabilistic search algorithms, such as Monte Carlo Tree Search (MCTS), have proven very effective in solving sequential decision-making tasks under uncertainty. However, interpre…

cs.CL2025

RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premises

Zenan Zhai, Hao Li, Xudong Han +4

Recent advances in large language models (LLMs) have shown that they can answer questions requiring complex reasoning. However, their ability to identify and respond to text contai…

cs.CL2024

Against The Achilles' Heel: A Survey on Red Teaming for Generative Models

Lizhi Lin, Honglin Mu, Zenan Zhai +9

Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed. In li…

cs.CL20242 cited

Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Renxi Wang, Haonan Li, Xudong Han +2

Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools such as search engines. However, LLMs are optimized for langu…

cs.CL20233 cited

Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE

Yixuan Zhang, Haonan Li

Large language models (LLMs) have showcased remarkable capabilities in understanding and generating language. However, their ability in comprehending ancient languages, particularl…